Database Processing

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Running head: DATABASE PROCESSING Database Processing Student’s Name Institutional Affiliation 1 DATABASE PROCESSING 2 Introduction The rows in the table shown below give record of the fact that all the employees from a specific project attended a meeting on a specific date. The assumptions that are made in the arrangement below is that the meetings are held once every day (Clark & Yuille, 2013). In addition to that, there is an assumption that only a single employee makes a representation of a given project; but all the same one employee can be given an assignment of different projects. Staff Meeting Name of Employee Name of Project Roger Whitaker Chemical X James McMahon Voltron Elizabeth Swan Mega Nadia Rodriguez Ultra Joanna Turnbull Moschino Jacqueline Thorngrass Mediamax Functional Dependencies in a Staff Meeting Name of Employee Date 6/28/2016 7/2/2016 7/2/2016 7/3/2016 7/4/2016 7/5/2016 Name of Project BOYCE-CODD NORMAL FORM Dependencies; Employee → Project — each and every employee works for a particular project; Project, Date → Employee — each and every particular project has a number of employees working on it and it is supposed to run on several dates; all the same every project happens to have a unique employee for each particular date (Kanehisa et. al, 2014). Staff Meeting Name of Employee Name of Project Date Roger Whitaker Chemical X 7/2/2016 James McMahon Voltron 8/2/2016 Elizabeth Swan Mega 8/2/2016 Nadia Rodriguez Ultra 8/3/2016 Joanna Turnbull Moschino 8/4/2016 Jacqueline Thorngrass Mediamax 8/5/2016 Determinants; candidate keys DB (Employee, Date, Meeting, Employee Assigned) Employee Project Name Employee, Employee Number Time There is a need to make a decision of what the primary keys of the database will be. From the data we have, we can select; Database DATABASE PROCESSING 3 (Employee Number, Employee Name, Project Number, Date) (Employee Name, Employee Number, Date, Project Number) Referential Integrity Constraints A referential integrity constraint is referred to as a section of an association between two types of entities. Many a times the proper definition of a referential integrity constraint gives specification to several information types; the principle end of the above mentioned constraint (a type of entity whereby the entity key is referenced by the end that is dependent. Advantages of BCNF The design In Part B Is an improvement over the original table. Boyce-Codd Normal Form tables bring about normalization; normalization brings about reduced data redundancy, captures complete business requirements, reduces data modification anomalies, and increases the quality of data (Kreinovich et. al, 2013). In every level of normalization, most data redundancy is removed from the above mentioned model. With BCNF, there is a reduced chance in the storage of unwanted information in the table above. Also, business needs become much easier to capture with Boyce-Codd Normal Form because it helps answer a lot of questions asked. The disadvantages of BOYCE-CODD NORMAL FORM The performance of the database becomes slower because there are a lot of number tables and joins in between them. Having a lot of tables uses much more development time when it comes to implementation. Fully normalized databases require a broad and clear comprehension of the business; there is a need for more time to make an analysis and to get to understand the nature of the business. Summary Overall, Boyce-Codd Normal Form is an invaluable tool when it comes to normalization and making information to be much more easy to comprehend for people looking to make an analysis of data. The advantages outweigh the disadvantages. DATABASE PROCESSING 4 References Clark, J. J., & Yuille, A. L. (2013). Data fusion for sensory information processing systems (Vol. 105). Springer Science & Business Media. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids research, 42(D1), D199-D205. Kreinovich, V., Lakeyev, A. V., Rohn, J., & Kahl, P. T. (2013). Computational complexity and feasibility of data processing and interval computations (Vol. 10). Springer Science & Business Media.

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